Automatic Pain Assessment by Learning from Multiple Biopotentials

Kivun täsmällinen arviointi on tärkeää kivunhallinnassa, erityisesti sairaan- hoitoa vaativille ipupotilaille. Kipu on subjektiivista, sillä se ei ole pelkästään aistituntemus, vaan siihen saattaa liittyä myös tunnekokemuksia. Tällöin itsearviointiin perustuvat kipuasteikot ovat tärkein työkalu, niin auan kun potilas pystyy kokemuksensa arvioimaan. Arviointi on kuitenkin haasteellista potilailla, jotka eivät itse pysty kertomaan kivustaan. Kliinisessä hoito- työssä kipua pyritään objektiivisesti arvioimaan esimerkiksi havainnoimalla fysiologisia muuttujia kuten sykettä ja käyttäytymistä esimerkiksi potilaan kasvonilmeiden perusteella. Tutkimuksen päätavoitteena on automatisoida arviointiprosessi hyödyntämällä koneoppimismenetelmiä yhdessä biosignaalien prosessointnin kanssa. Tavoitteen saavuttamiseksi mitattiin autonomista keskushermoston toimintaa kuvastavia biopotentiaaleja: sydänsähkökäyrää, galvaanista ihoreaktiota ja kasvolihasliikkeitä mittaavaa lihassähkökäyrää. Mittaukset tehtiin terveillä vapaaehtoisilla, joille aiheutettiin kokeellista kipuärsykettä. Järestelmän kehittämiseen tarvittavaa tietokantaa varten rakennettiin biopotentiaaleja keräävä Internet of Things -pohjainen tallennusjärjestelmä. Koostetun tietokannan avulla kehitettiin biosignaaleille prosessointimenetelmä jatku- vaan kivun arviointiin. Signaaleista eroteltiin piirteitä sekuntitasoon mukautetuilla aikaikkunoilla. Piirteet visualisoitiin ja tarkasteltiin eri luokittelijoilla kivun ja kiputason tunnistamiseksi. Parhailla luokittelumenetelmillä saavutettiin kivuntunnistukseen 90% herkkyyskyky (sensitivity) ja 84% erottelukyky (specificity) ja kivun voimakkuuden arviointiin 62,5% tarkkuus (accuracy). Tulokset vahvistavat kyseisen käsittelytavan käyttökelpoisuuden erityis- esti tunnistettaessa kipua yksittäisessä arviointi-ikkunassa. Tutkimus vahvistaa biopotentiaalien avulla kehitettävän automatisoidun kivun arvioinnin toteutettavuuden kokeellisella kivulla, rohkaisten etenemään todellisen kivun tutkimiseen samoilla menetelmillä. Menetelmää kehitettäessä suoritettiin lisäksi vertailua ja yhteenvetoa automaattiseen kivuntunnistukseen kehitettyjen eri tutkimusten välisistä samankaltaisuuksista ja eroista. Tarkastelussa löytyi signaalien eroavaisuuksien lisäksi tutkimusmuotojen aiheuttamaa eroa arviointitavoitteisiin, mikä hankaloitti tutkimusten vertailua. Lisäksi pohdit- tiin mitkä perinteisten prosessointitapojen osiot rajoittavat tai edistävät ennustekykyä ja miten, sekä tuoko optimointi läpimurtoa järjestelmän näkökulmasta.Accurate pain assessment plays an important role in proper pain management, especially among hospitalized people experience acute pain. Pain is subjective in nature which is not only a sensory feeling but could also combine affective factors. Therefore self-report pain scales are the main assessment tools as long as patients are able to self-report. However, it remains a challenge to assess the pain from the patients who cannot self-report. In clinical practice, physiological parameters like heart rate and pain behaviors including facial expressions are observed as empirical references to infer pain objectively. The main aim of this study is to automate such process by leveraging machine learning methods and biosignal processing. To achieve this goal, biopotentials reflecting autonomic nervous system activities including electrocardiogram and galvanic skin response, and facial expressions measured with facial electromyograms were recorded from healthy volunteers undergoing experimental pain stimulus. IoT-enabled biopotential acquisition systems were developed to build the database aiming at providing compact and wearable solutions. Using the database, a biosignal processing flow was developed for continuous pain estimation. Signal features were extracted with customized time window lengths and updated every second. The extracted features were visualized and fed into multiple classifiers trained to estimate the presence of pain and pain intensity separately. Among the tested classifiers, the best pain presence estimating sensitivity achieved was 90% (specificity 84%) and the best pain intensity estimation accuracy achieved was 62.5%. The results show the validity of the proposed processing flow, especially in pain presence estimation at window level. This study adds one more piece of evidence on the feasibility of developing an automatic pain assessment tool from biopotentials, thus providing the confidence to move forward to real pain cases. In addition to the method development, the similarities and differences between automatic pain assessment studies were compared and summarized. It was found that in addition to the diversity of signals, the estimation goals also differed as a result of different study designs which made cross dataset comparison challenging. We also tried to discuss which parts in the classical processing flow would limit or boost the prediction performance and whether optimization can bring a breakthrough from the system’s perspective

A Comprehensive Study on Pain Assessment from Multimodal Sensor Data

Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed

Postoperative pain assessment methods in infants and young children: a review.

Pain assessment in a pediatric postoperative setting has always been challenging, due to the lack of insight about pain mechanisms in newborns, infants and young children. Several research works about this subject were conducted over the years, and such studies contradict what was postulated for many years and demonstrate that infants do indeed feel pain stimuli, even more so than older children or adults. For this reason, it is important for health care providers to be familiar with the best pediatric pain assessment tools available at the moment, accordingly to age, cognitive development and context of the pain (in this case, a postoperative setting). Therefore, this paper will focus on the diverse available scales and parameters used at the present time, as well as their advantages and limitations. Furthermore, some recent developed technologies are briefly mentioned, some of which could in the future translate a solution for this problem. At the present time there is no gold standard for pain assessment in all clinical settings and pediatric age groups. Self-report, behavioural and physiological scales can be used for such propose, although none of these methods has proven to be superior or demonstrated excellent accuracy. Moreover, further research is needed in order to achieve the discovery and validation of an objective and easy to use pain assessment instrument, that could be implemented as a gold standard for worldwide use. The question about the best pain assessment method for infants and young children remains unanswered, being necessary to adapt the pain assessment process to each specific child and context

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15/00306Junta de Andalucía PIN-0394-2017Unión Europea "FRAIL

Development of Markerless Systems for Automatic Analysis of Movements and Facial Expressions: Applications in Neurophysiology

This project is focused on the development of markerless methods for studying facial expressions and movements in neurology, focusing on Parkinson’s disease (PD) and disorders of consciousness (DOC). PD is a neurodegenerative illness that affects around 2% of the population over 65 years old. Impairments of voice/speech are among the main signs of PD. This set of impairments is called hypokinetic dysarthria, because of the reduced range of movements involved in speech. This reduction can be visible also in other facial muscles, leading to a hypomimia. Despite the high percentage of patients that suffer from dysarthria and hypomimia, only a few of them undergo speech therapy with the aim to improve the dynamic of articulatory/facial movements. The main reason is the lack of low cost methodologies that could be implemented at home. DOC after coma are Vegetative State (VS), characterized by the absence of self-awareness and awareness of the environment, and Minimally Conscious State (MCS), in which certain behaviors are sufficiently reproducible to be distinguished from reflex responses. The differential diagnosis between VS and MCS can be hard and prone to a high rate of misdiagnosis (~40%). This differential diagnosis is mainly based on neuro-behavioral scales. A key role to plan the rehabilitation in DOC patients is played by the first diagnosis after coma. In fact, MCS patients are more prone to a consciousness recovery than VS patients. Concerning PD the aim is the development of contactless systems that could be used to study symptoms related to speech and facial movements/expressions. The methods proposed here, based on acoustical analysis and video processing techniques could support patients during speech therapy also at home. Concerning DOC patients the project is focused on the assessment of reflex and cognitive responses to standardized stimuli. This would allow objectifying the perceptual analysis performed by clinicians

Development of a Composite Pain Scale in Foals: A Pilot Study

open7siThis research was funded by International Society for Equitation Science (ISES) as awarded of the ISES Student Research Grant 2020.Prompt pain management is crucial in horses; however, tools to assess pain are limited. This study aimed to develop and pilot a composite scale for pain estimation in foals. The “Foal Composite Pain Scale” (FCPS) was developed based on literature and authors’ expertise. The FCPS consisted of 11 facial expressions, 4 behavioural items, and 5 physical items. Thirty-five pain-free foals (Control Group) and 15 foals experiencing pain (Pain Group) were used. Foals were video-recorded at different time points: the Control Group only at inclusion (C), while the Pain Group at inclusion (T1), after an analgesic treatment (T2), and at recovery (T3). Physical items were also recorded at the same time points. Videos were scored twice by five trained observers, blinded to group and time points, to calculate inter-and intra-observer reliability of each scale item. Fleiss’ kappa values ranged from moderate to almost perfect for the majority of the items, while the intraclass correlation coefficient was excellent (ICC = 0.923). The consistency of FCPS was also excellent (Cronbach’s alpha = 0.842). A cut-off ≥ 7 indicated the presence of pain. The Pain Group scores were significantly higher (p < 0.001) than the Control Group and decreased over time (T1, T2 > T3; p = 0.001). Overall, FCPS seems clinically applicable to quantify pain and improve the judgment of the quality of life in foals, but it needs modifications based on these preliminary findings. Consequently, further studies on a larger sample size are needed to test the feasibility and validity of the refined FCPS.openLanci A.; Benedetti B.; Freccero F.; Castagnetti C.; Mariella J.; van Loon J.P.A.M.; Padalino B.Lanci A.; Benedetti B.; Freccero F.; Castagnetti C.; Mariella J.; van Loon J.P.A.M.; Padalino B

Acute Pain Symptom Assessment and Management in Nonverbal Puerto Rican Patients in the Early Postoperative Period

Acute pain is a symptom that represents significant concern for surgical patients during the early postoperative period. This is probably due to the use of ineffective instruments or protocols for assessment in patients with different levels of sedation after general anesthesia. This study described the relationships between the total scores obtained from two pain assessment instruments, the Non-verbal Pain Scale Revised (NVPS-R) and Critical Care Pain Observation Tool (CPOT), during the early postoperative period for non-verbal patients at Post Anesthesia Care Unit (PACU). After assessing patient’s pain with both instruments simultaneously, we determined, and evaluated the relationships between the two behavioral instruments to assess acute pain in the early postoperative period. Recent literature confirmed the research gap in assessment of postoperative pain in nonverbal patients due to inadequate evidence to guide recommendations about which specific non-verbal pain instrument to use. The results of this study present a high correlation between total pain scores of both behavioral assessment instruments, CPOT and NVPS-R, for postoperative patients after abdomino-pelvic, gastrointestinal and gynecological surgeries. Incidental findings suggest that CPOT vocalization indicator was consistently present in patients with significant pain. Increases in the NVPS-R vital signs and respiratory indicators were not seen consistently in patients with significant pain. The vital sign indicators included in the NVPS-R need to be further investigated to determine their validity for assuring pain assessment because our findings do not support their use exclusively. Physiologic indicators as heart rate, mean arterial pressure, respiratory rate and pulse oxygen saturation were not good indicators of acute pain. This study is important because pain assessment is the best way to initiate the most appropriate treatment to alleviate pain after surgery. Institutions where surgeries are performed need to standardize and provide clear policies and procedures for effective postoperative pain assessment and management. Healthcare providers are patient advocates and a clear vision in providing the most effective management contributes to decrease the worldwide problem of under treatment of acute pain